Investigation on eXtreme Gradient Boosting for cutting force prediction in milling

• Accurate prediction of cutting forces is critical in milling operations, with implications for cost reduction and improved manufacturing efficiency. While traditional mechanistic models provide high accuracy, their reliance on extensive milling data for force coefficient fitting poses challenges. The eXtreme Gradient Boosting algorithm offers a potential solution with reduced data requirements, yet the optimal utilization of eXtreme Gradient Boosting remains unexplored. This study investigates its effectiveness in predicting cutting forces during down-milling of Al2024. A novel framework is proposed optimizing its precision, efficiency, and user-friendliness. The model training incorporates the mechanistic force model in both time and frequency domains as new features. Through rigorous experimentation, various aspects of the eXtreme Gradient Boosting configuration are explored, including identifying the optimal number of periods for the training dataset, determining the best normalization and scaling technique, and assessing the hyperparameters’ impact on model performance in terms of accuracy and computational time. The results show the remarkable effectiveness of the eXtreme Gradient Boosting model with an average normalized root mean square error of 14.7%, surpassing the 21.9% obtained by the mechanistic force model. Additionally, the machine learning model could capture the runout effect. These findings enable optimized milling operations regarding cost, accuracy and computation time.

Ämnesord

NATURVETENSKAP  -- Fysik -- Annan fysik (hsv//swe)

NATURAL SCIENCES  -- Physical Sciences -- Other Physics Topics (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Cutting force prediction

Machine learning

Milling

Optimization

XGBoost

Skövde Artificial Intelligence Lab (SAIL)

Skövde Artificial Intelligence Lab (SAIL)

INF301 Data Science

INF301 Data Science

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